Making the codebook

Here, we’re just setting a few options.

knitr::opts_chunk$set(
  warning = TRUE, # show warnings during codebook generation
  message = TRUE, # show messages during codebook generation
  error = TRUE, # do not interrupt codebook generation in case of errors,
                # usually better for debugging
  echo = TRUE  # show R code
)
ggplot2::theme_set(ggplot2::theme_bw())
## Warning: replacing previous import 'vctrs::data_frame' by 'tibble::data_frame'
## when loading 'dplyr'

Now, we’re preparing our data for the codebook.

library(codebook)
webshot::install_phantomjs()
## It seems that the version of `phantomjs` installed is greater than or equal to the requested version.To install the requested version or downgrade to another version, use `force = TRUE`.
library(labelled)
## 
## Attaching package: 'labelled'
## The following object is masked from 'package:codebook':
## 
##     to_factor
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
# codebook_data <- codebook::bfi
# to import an SPSS file from the same folder uncomment and edit the line below
# codebook_data <- rio::import("mydata.sav")
# for Stata
# codebook_data <- rio::import("mydata.dta")
# for CSV
codebook_data_prep <- rio::import("peril_data_deid.csv") 
v1 <- which(names(codebook_data_prep) == 'control_1') #find first column
v2 <- which(names(codebook_data_prep) == 'control_deep') #find last column

codebook_data <- codebook_data_prep %>%
  mutate_at(c(v1:v2),as.numeric)
## Warning: Problem with `mutate()` input `control_1`.
## x NAs introduced by coercion
## ℹ Input `control_1` is `.Primitive("as.double")(control_1)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `control_2`.
## x NAs introduced by coercion
## ℹ Input `control_2` is `.Primitive("as.double")(control_2)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `fam1`.
## x NAs introduced by coercion
## ℹ Input `fam1` is `.Primitive("as.double")(fam1)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `fam2`.
## x NAs introduced by coercion
## ℹ Input `fam2` is `.Primitive("as.double")(fam2)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `fam3`.
## x NAs introduced by coercion
## ℹ Input `fam3` is `.Primitive("as.double")(fam3)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `fam4`.
## x NAs introduced by coercion
## ℹ Input `fam4` is `.Primitive("as.double")(fam4)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `fam5`.
## x NAs introduced by coercion
## ℹ Input `fam5` is `.Primitive("as.double")(fam5)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `fam6`.
## x NAs introduced by coercion
## ℹ Input `fam6` is `.Primitive("as.double")(fam6)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `test1`.
## x NAs introduced by coercion
## ℹ Input `test1` is `.Primitive("as.double")(test1)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `test2`.
## x NAs introduced by coercion
## ℹ Input `test2` is `.Primitive("as.double")(test2)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `test3`.
## x NAs introduced by coercion
## ℹ Input `test3` is `.Primitive("as.double")(test3)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `test4`.
## x NAs introduced by coercion
## ℹ Input `test4` is `.Primitive("as.double")(test4)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `lower1`.
## x NAs introduced by coercion
## ℹ Input `lower1` is `.Primitive("as.double")(lower1)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `lower2`.
## x NAs introduced by coercion
## ℹ Input `lower2` is `.Primitive("as.double")(lower2)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `higher1`.
## x NAs introduced by coercion
## ℹ Input `higher1` is `.Primitive("as.double")(higher1)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `higher2`.
## x NAs introduced by coercion
## ℹ Input `higher2` is `.Primitive("as.double")(higher2)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `control_shallow`.
## x NAs introduced by coercion
## ℹ Input `control_shallow` is `.Primitive("as.double")(control_shallow)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
## Warning: Problem with `mutate()` input `control_deep`.
## x NAs introduced by coercion
## ℹ Input `control_deep` is `.Primitive("as.double")(control_deep)`.
## Caused by warning:
## ! NAs introduced by coercion
## Warning in mask$eval_all_mutate(dots[[i]]): NAs introduced by coercion
codebook_dictionary <- rio::import("peril_data_deid_codebook.csv")

var_label(codebook_data) <- codebook_dictionary %>% select(variable, label) %>% dict_to_list()

metadata(codebook_data)$name <- 'Dataset Codebook'
metadata(codebook_data)$description <- "Data associated with paper 'Dangerous ground: One-year-old infants are sensitive to peril in other agents’ action plans'"
metadata(codebook_data)$creator <- "Shari Liu"
metadata(codebook_data)$datePublished <- "2022-04-12"

# omit the following lines, if your missing values are already properly labelled
# codebook_data <- detect_missing(codebook_data,
#     only_labelled = TRUE, # only labelled values are autodetected as
#                                    # missing
#     negative_values_are_missing = FALSE, # negative values are missing values
#     ninety_nine_problems = TRUE,   # 99/999 are missing values, if they
#                                    # are more than 5 MAD from the median
#     )

# If you are not using formr, the codebook package needs to guess which items
# form a scale. The following line finds item aggregates with names like this:
# scale = scale_1 + scale_2R + scale_3R
# identifying these aggregates allows the codebook function to
# automatically compute reliabilities.
# However, it will not reverse items automatically.
# codebook_data <- detect_scales(codebook_data)

Create codebook

skim_codebook(codebook_data)
Data summary
Name data
Number of rows 286
Number of columns 40
_______________________
Column type frequency:
character 13
numeric 27
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
sex 0 1 1 1 0 2 0
subj 0 1 1 6 0 286 0
experiment 0 1 3 10 0 9 0
exp_oldmapping 0 1 5 10 0 8 0
exp 0 1 5 11 0 9 0
cost 0 1 3 8 0 4 0
device 0 1 0 6 204 3 0
HV_side 0 1 0 5 102 7 0
first_test 0 1 2 7 0 4 0
first_fam 0 1 2 4 0 4 0
first_test_deeper_side 0 1 0 5 144 3 0
control_deeper_side 0 1 0 5 144 3 0
control_firstevent 0 1 0 7 144 3 0

Variable type: numeric

skim_variable n_missing complete_rate mean sd min median max hist
reliability 80 0.72 0.50 0.50 0.00 0.00 1.00 ▇▁▁▁▇
agem 0 1.00 11.24 1.67 8.97 10.33 15.67 ▇▂▃▂▁
video_quality 206 0.28 4.88 0.33 4.00 5.00 5.00 ▁▁▁▁▇
audio_quality 206 0.28 4.86 0.35 4.00 5.00 5.00 ▁▁▁▁▇
highchair 204 0.29 0.46 0.50 0.00 0.00 1.00 ▇▁▁▁▇
control_1 155 0.46 14.85 7.68 3.84 13.02 47.24 ▇▆▂▁▁
control_2 155 0.46 11.79 8.61 1.36 9.05 53.69 ▇▃▁▁▁
fam1 4 0.99 55.02 11.33 11.20 60.00 60.00 ▁▁▁▁▇
fam2 7 0.98 45.43 18.85 4.09 58.70 60.00 ▂▂▁▁▇
fam3 8 0.97 37.85 20.51 2.67 40.45 60.00 ▃▃▂▂▇
fam4 9 0.97 30.69 20.40 2.10 23.60 60.00 ▇▆▃▃▇
fam5 10 0.97 25.34 18.64 1.60 20.49 60.00 ▇▆▃▂▅
fam6 10 0.97 21.16 17.53 2.00 14.04 60.00 ▇▃▂▁▂
test1 9 0.97 26.16 16.90 3.31 21.15 60.00 ▇▆▃▂▃
test2 21 0.93 21.97 16.12 3.73 16.80 60.00 ▇▅▂▂▂
test3 40 0.86 19.10 15.45 1.71 13.25 60.00 ▇▃▂▁▁
test4 45 0.84 19.50 15.41 2.79 13.34 60.00 ▇▃▁▁▁
avg_fam 1 1.00 36.00 11.41 8.72 34.96 60.00 ▂▃▇▆▂
sum_fam 1 1.00 210.70 69.01 52.30 206.30 360.00 ▂▅▇▆▂
testavg_lower 0 1.00 22.04 13.37 3.73 17.98 60.00 ▇▆▃▂▁
testavg_higher 0 1.00 21.45 13.37 2.51 17.31 60.00 ▇▇▅▂▂
lower1 14 0.95 24.85 16.89 3.67 19.43 60.00 ▇▆▃▂▂
lower2 43 0.85 19.56 15.66 2.79 13.57 60.00 ▇▃▂▁▁
higher1 16 0.94 23.36 16.38 3.31 17.98 60.00 ▇▆▂▂▂
higher2 42 0.85 19.04 15.20 1.71 13.27 60.00 ▇▃▁▁▁
control_shallow 154 0.46 14.11 8.68 1.61 11.81 53.69 ▇▆▂▁▁
control_deep 156 0.45 12.56 7.83 1.36 10.27 39.93 ▇▇▃▁▁
codebook(codebook_data)

Metadata

Description

Dataset name: Dataset Codebook

Data associated with paper ‘Dangerous ground: One-year-old infants are sensitive to peril in other agents’ action plans’

Metadata for search engines

  • Date published: 2022-04-12

  • Creator:

name value
1 Shari Liu
x
reliability
sex
subj
agem
experiment
exp_oldmapping
exp
cost
video_quality
audio_quality
device
highchair
HV_side
first_test
first_fam
first_test_deeper_side
control_deeper_side
control_firstevent
control_1
control_2
fam1
fam2
fam3
fam4
fam5
fam6
test1
test2
test3
test4
avg_fam
sum_fam
testavg_lower
testavg_higher
lower1
lower2
higher1
higher2
control_shallow
control_deep

#Variables

reliability

whether this participant was randomly chosen for reliability coding

Distribution

Distribution of values for reliability

Distribution of values for reliability

80 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
reliability whether this participant was randomly chosen for reliability coding numeric 80 0.7202797 0 0 1 0.4951456 0.5011944 ▇▁▁▁▇

sex

male or female

Distribution

Distribution of values for sex

Distribution of values for sex

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
sex male or female character 0 1 2 0 1 1 0

subj

anonymized subject identity

Distribution

Distribution of values for subj

Distribution of values for subj

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
subj anonymized subject identity character 0 1 286 0 1 6 0

agem

age in months

Distribution

Distribution of values for agem

Distribution of values for agem

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
agem age in months numeric 0 1 9 10 16 11.24014 1.670372 ▇▂▃▂▁

experiment

original name of experiment

Distribution

Distribution of values for experiment

Distribution of values for experiment

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
experiment original name of experiment character 0 1 9 0 3 10 0

exp_oldmapping

older, unused experiment naming in previous version of the paper

Distribution

Distribution of values for exp_oldmapping

Distribution of values for exp_oldmapping

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
exp_oldmapping older, unused experiment naming in previous version of the paper character 0 1 8 0 5 10 0

exp

up to date name of experiment used in the paper

Distribution

Distribution of values for exp

Distribution of values for exp

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
exp up to date name of experiment used in the paper character 0 1 9 0 5 11 0

cost

what kind of obstacle agent overcame in experiment (e.g. barrier, ramp, gap in LUTS, or danger in this paper)

Distribution

Distribution of values for cost

Distribution of values for cost

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
cost what kind of obstacle agent overcame in experiment (e.g. barrier, ramp, gap in LUTS, or danger in this paper) character 0 1 4 0 3 8 0

video_quality

for online studies, caregiver rating of quality of stimulus videos

Distribution

Distribution of values for video_quality

Distribution of values for video_quality

206 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
video_quality for online studies, caregiver rating of quality of stimulus videos numeric 206 0.2797203 4 5 5 4.875 0.3328055 ▁▁▁▁▇

audio_quality

for online studies, caregiver rating of quality of stimulus sound

Distribution

Distribution of values for audio_quality

Distribution of values for audio_quality

206 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
audio_quality for online studies, caregiver rating of quality of stimulus sound numeric 206 0.2797203 4 5 5 4.85625 0.348539 ▁▁▁▁▇

device

for online studies, what device was used to view the stimuli

Distribution

Distribution of values for device

Distribution of values for device

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
device for online studies, what device was used to view the stimuli character 0 1 3 204 0 6 0

highchair

for online studies, whether baby sat in a high chair for the duration of the experiment

Distribution

Distribution of values for highchair

Distribution of values for highchair

204 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
highchair for online studies, whether baby sat in a high chair for the duration of the experiment numeric 204 0.2867133 0 0 1 0.4634146 0.5017284 ▇▁▁▁▇

HV_side

which side the higher value agent was on, or what side the deeper cliff was on

Distribution

Distribution of values for HV_side

Distribution of values for HV_side

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
HV_side which side the higher value agent was on, or what side the deeper cliff was on character 0 1 7 102 0 5 0

first_test

first test event

Distribution

Distribution of values for first_test

Distribution of values for first_test

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
first_test first test event character 0 1 4 0 2 7 0

first_fam

first familiarization event

Distribution

Distribution of values for first_fam

Distribution of values for first_fam

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
first_fam first familiarization event character 0 1 4 0 2 4 0

first_test_deeper_side

for Exp 2, the left-right arrangement of the deeper and shallower trench during the first test trial

Distribution

Distribution of values for first_test_deeper_side

Distribution of values for first_test_deeper_side

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
first_test_deeper_side for Exp 2, the left-right arrangement of the deeper and shallower trench during the first test trial character 0 1 3 144 0 5 0

control_deeper_side

the left-right arrangement of the deeper and shallower trench during the control event

Distribution

Distribution of values for control_deeper_side

Distribution of values for control_deeper_side

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
control_deeper_side the left-right arrangement of the deeper and shallower trench during the control event character 0 1 3 144 0 5 0

control_firstevent

which event came first during control event

Distribution

Distribution of values for control_firstevent

Distribution of values for control_firstevent

0 missing values.

Summary statistics

name label data_type n_missing complete_rate n_unique empty min max whitespace
control_firstevent which event came first during control event character 0 1 3 144 0 7 0

control_1

looking time during first control event

Distribution

Distribution of values for control_1

Distribution of values for control_1

155 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
control_1 looking time during first control event numeric 155 0.458042 3.8 13 47 14.85113 7.675728 ▇▆▂▁▁

control_2

looking time during second control event

Distribution

Distribution of values for control_2

Distribution of values for control_2

155 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
control_2 looking time during second control event numeric 155 0.458042 1.4 9.1 54 11.79247 8.607699 ▇▃▁▁▁

fam1

looking time during familiarization

Distribution

Distribution of values for fam1

Distribution of values for fam1

4 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fam1 looking time during familiarization numeric 4 0.986014 11 60 60 55.01654 11.33048 ▁▁▁▁▇

fam2

looking time during familiarization

Distribution

Distribution of values for fam2

Distribution of values for fam2

7 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fam2 looking time during familiarization numeric 7 0.9755245 4.1 59 60 45.42599 18.84502 ▂▂▁▁▇

fam3

looking time during familiarization

Distribution

Distribution of values for fam3

Distribution of values for fam3

8 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fam3 looking time during familiarization numeric 8 0.972028 2.7 40 60 37.85457 20.50916 ▃▃▂▂▇

fam4

looking time during familiarization

Distribution

Distribution of values for fam4

Distribution of values for fam4

9 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fam4 looking time during familiarization numeric 9 0.9685315 2.1 24 60 30.69409 20.4046 ▇▆▃▃▇

fam5

looking time during familiarization

Distribution

Distribution of values for fam5

Distribution of values for fam5

10 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fam5 looking time during familiarization numeric 10 0.965035 1.6 20 60 25.3413 18.639 ▇▆▃▂▅

fam6

looking time during familiarization

Distribution

Distribution of values for fam6

Distribution of values for fam6

10 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
fam6 looking time during familiarization numeric 10 0.965035 2 14 60 21.16081 17.525 ▇▃▂▁▂

test1

looking time during test

Distribution

Distribution of values for test1

Distribution of values for test1

9 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
test1 looking time during test numeric 9 0.9685315 3.3 21 60 26.15703 16.89635 ▇▆▃▂▃

test2

looking time during test

Distribution

Distribution of values for test2

Distribution of values for test2

21 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
test2 looking time during test numeric 21 0.9265734 3.7 17 60 21.97015 16.11725 ▇▅▂▂▂

test3

looking time during test

Distribution

Distribution of values for test3

Distribution of values for test3

40 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
test3 looking time during test numeric 40 0.8601399 1.7 13 60 19.10359 15.44814 ▇▃▂▁▁

test4

looking time during test

Distribution

Distribution of values for test4

Distribution of values for test4

45 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
test4 looking time during test numeric 45 0.8426573 2.8 13 60 19.50339 15.41324 ▇▃▁▁▁

avg_fam

average looking time during familiarization (fam 1-6)

Distribution

Distribution of values for avg_fam

Distribution of values for avg_fam

1 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
avg_fam average looking time during familiarization (fam 1-6) numeric 1 0.9965035 8.7 35 60 36.00463 11.41305 ▂▃▇▆▂

sum_fam

total looking time during familiarization (fam 1-6)

Distribution

Distribution of values for sum_fam

Distribution of values for sum_fam

1 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
sum_fam total looking time during familiarization (fam 1-6) numeric 1 0.9965035 52 206 360 210.698 69.01051 ▂▅▇▆▂

testavg_lower

average looking time towards the lower value or lower danger test event

Distribution

Distribution of values for testavg_lower

Distribution of values for testavg_lower

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
testavg_lower average looking time towards the lower value or lower danger test event numeric 0 1 3.7 18 60 22.04001 13.37239 ▇▆▃▂▁

testavg_higher

average looking time towards the higher value or higher danger test event

Distribution

Distribution of values for testavg_higher

Distribution of values for testavg_higher

0 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
testavg_higher average looking time towards the higher value or higher danger test event numeric 0 1 2.5 17 60 21.44526 13.37161 ▇▇▅▂▂

lower1

looking time during the first lower value or lower danger test event

Distribution

Distribution of values for lower1

Distribution of values for lower1

14 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
lower1 looking time during the first lower value or lower danger test event numeric 14 0.951049 3.7 19 60 24.85424 16.88764 ▇▆▃▂▂

lower2

looking time during the seoncd lower value or lower danger test event

Distribution

Distribution of values for lower2

Distribution of values for lower2

43 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
lower2 looking time during the seoncd lower value or lower danger test event numeric 43 0.8496503 2.8 14 60 19.56239 15.659 ▇▃▂▁▁

higher1

looking time during the first higher value or higher danger test event

Distribution

Distribution of values for higher1

Distribution of values for higher1

16 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
higher1 looking time during the first higher value or higher danger test event numeric 16 0.9440559 3.3 18 60 23.36013 16.37809 ▇▆▂▂▂

higher2

looking time during the second higher value or higher danger test event

Distribution

Distribution of values for higher2

Distribution of values for higher2

42 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
higher2 looking time during the second higher value or higher danger test event numeric 42 0.8531469 1.7 13 60 19.04155 15.19846 ▇▃▁▁▁

control_shallow

looking time during control event involving shallow cliff

Distribution

Distribution of values for control_shallow

Distribution of values for control_shallow

154 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
control_shallow looking time during control event involving shallow cliff numeric 154 0.4615385 1.6 12 54 14.10883 8.681716 ▇▆▂▁▁

control_deep

looking time during control event involving deeper cliff

Distribution

Distribution of values for control_deep

Distribution of values for control_deep

156 missing values.

Summary statistics

name label data_type n_missing complete_rate min median max mean sd hist
control_deep looking time during control event involving deeper cliff numeric 156 0.4545455 1.4 10 40 12.56002 7.832928 ▇▇▃▁▁

Missingness report

Codebook table

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{
  "name": "Dataset Codebook",
  "description": "Data associated with paper 'Dangerous ground: One-year-old infants are sensitive to peril in other agents’ action plans'\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n[truncated]\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "creator": "Shari Liu",
  "datePublished": "2022-04-12",
  "keywords": ["reliability", "sex", "subj", "agem", "experiment", "exp_oldmapping", "exp", "cost", "video_quality", "audio_quality", "device", "highchair", "HV_side", "first_test", "first_fam", "first_test_deeper_side", "control_deeper_side", "control_firstevent", "control_1", "control_2", "fam1", "fam2", "fam3", "fam4", "fam5", "fam6", "test1", "test2", "test3", "test4", "avg_fam", "sum_fam", "testavg_lower", "testavg_higher", "lower1", "lower2", "higher1", "higher2", "control_shallow", "control_deep"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "reliability",
      "description": "whether this participant was randomly chosen for reliability coding",
      "@type": "propertyValue"
    },
    {
      "name": "sex",
      "description": "male or female",
      "@type": "propertyValue"
    },
    {
      "name": "subj",
      "description": "anonymized subject identity",
      "@type": "propertyValue"
    },
    {
      "name": "agem",
      "description": "age in months",
      "@type": "propertyValue"
    },
    {
      "name": "experiment",
      "description": "original name of experiment",
      "@type": "propertyValue"
    },
    {
      "name": "exp_oldmapping",
      "description": "older, unused experiment naming in previous version of the paper",
      "@type": "propertyValue"
    },
    {
      "name": "exp",
      "description": "up to date name of experiment used in the paper",
      "@type": "propertyValue"
    },
    {
      "name": "cost",
      "description": "what kind of obstacle agent overcame in experiment (e.g. barrier, ramp, gap in LUTS, or danger in this paper)",
      "@type": "propertyValue"
    },
    {
      "name": "video_quality",
      "description": "for online studies, caregiver rating of quality of stimulus videos",
      "@type": "propertyValue"
    },
    {
      "name": "audio_quality",
      "description": "for online studies, caregiver rating of quality of stimulus sound",
      "@type": "propertyValue"
    },
    {
      "name": "device",
      "description": "for online studies, what device was used to view the stimuli",
      "@type": "propertyValue"
    },
    {
      "name": "highchair",
      "description": "for online studies, whether baby sat in a high chair for the duration of the experiment",
      "@type": "propertyValue"
    },
    {
      "name": "HV_side",
      "description": "which side the higher value agent was on, or what side the deeper cliff was on",
      "@type": "propertyValue"
    },
    {
      "name": "first_test",
      "description": "first test event",
      "@type": "propertyValue"
    },
    {
      "name": "first_fam",
      "description": "first familiarization event",
      "@type": "propertyValue"
    },
    {
      "name": "first_test_deeper_side",
      "description": "for Exp 2, the left-right arrangement of the deeper and shallower trench during the first test trial",
      "@type": "propertyValue"
    },
    {
      "name": "control_deeper_side",
      "description": "the left-right arrangement of the deeper and shallower trench during the control event",
      "@type": "propertyValue"
    },
    {
      "name": "control_firstevent",
      "description": "which event came first during control event",
      "@type": "propertyValue"
    },
    {
      "name": "control_1",
      "description": "looking time during first control event",
      "@type": "propertyValue"
    },
    {
      "name": "control_2",
      "description": "looking time during second control event",
      "@type": "propertyValue"
    },
    {
      "name": "fam1",
      "description": "looking time during familiarization",
      "@type": "propertyValue"
    },
    {
      "name": "fam2",
      "description": "looking time during familiarization",
      "@type": "propertyValue"
    },
    {
      "name": "fam3",
      "description": "looking time during familiarization",
      "@type": "propertyValue"
    },
    {
      "name": "fam4",
      "description": "looking time during familiarization",
      "@type": "propertyValue"
    },
    {
      "name": "fam5",
      "description": "looking time during familiarization",
      "@type": "propertyValue"
    },
    {
      "name": "fam6",
      "description": "looking time during familiarization",
      "@type": "propertyValue"
    },
    {
      "name": "test1",
      "description": "looking time during test",
      "@type": "propertyValue"
    },
    {
      "name": "test2",
      "description": "looking time during test",
      "@type": "propertyValue"
    },
    {
      "name": "test3",
      "description": "looking time during test",
      "@type": "propertyValue"
    },
    {
      "name": "test4",
      "description": "looking time during test",
      "@type": "propertyValue"
    },
    {
      "name": "avg_fam",
      "description": "average looking time during familiarization (fam 1-6)",
      "@type": "propertyValue"
    },
    {
      "name": "sum_fam",
      "description": "total looking time during familiarization (fam 1-6)",
      "@type": "propertyValue"
    },
    {
      "name": "testavg_lower",
      "description": "average looking time towards the lower value or lower danger test event",
      "@type": "propertyValue"
    },
    {
      "name": "testavg_higher",
      "description": "average looking time towards the higher value or higher danger test event",
      "@type": "propertyValue"
    },
    {
      "name": "lower1",
      "description": "looking time during the first lower value or lower danger test event",
      "@type": "propertyValue"
    },
    {
      "name": "lower2",
      "description": "looking time during the seoncd lower value or lower danger test event",
      "@type": "propertyValue"
    },
    {
      "name": "higher1",
      "description": "looking time during the first higher value or higher danger test event",
      "@type": "propertyValue"
    },
    {
      "name": "higher2",
      "description": "looking time during the second higher value or higher danger test event",
      "@type": "propertyValue"
    },
    {
      "name": "control_shallow",
      "description": "looking time during control event involving shallow cliff",
      "@type": "propertyValue"
    },
    {
      "name": "control_deep",
      "description": "looking time during control event involving deeper cliff",
      "@type": "propertyValue"
    }
  ]
}`